# Loading packages
library(tidyverse)
library(lubridate)
library(janitor)
library(skimr)
library(corrplot)
library(haven)
library(ggpubr)
library(sjlabelled)
library(sjPlot)
# Create new dataset
combined_data <- read_csv(here::here("Data/combined_final_data.csv"))
No Pre-BMI adjustments, IEAA
## No Pre-BMI adjustments, IEAA
ieaa_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ ieaa_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)
ieaa_pregresid_no_bmi_icc_length_home <- update(ieaa_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)
ieaa_pregresid_no_bmi_icc_armcircm_home <- update(ieaa_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)
ieaa_pregresid_no_bmi_icc_abdom_mean_home <- update(ieaa_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
ieaa_pregresid_no_bmi_icc_headcirc_mean_home <- update(ieaa_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
ieaa_pregresid_no_bmi_icc_total_skin_mean_home <- update(ieaa_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(ieaa_pregresid_no_bmi_icc_weight_home, ieaa_pregresid_no_bmi_icc_length_home, ieaa_pregresid_no_bmi_icc_armcircm_home, ieaa_pregresid_no_bmi_icc_abdom_mean_home, ieaa_pregresid_no_bmi_icc_headcirc_mean_home, ieaa_pregresid_no_bmi_icc_total_skin_mean_home)
## No Pre-BMI adjustments, EEAA
eeaa_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ eeaa_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)
eeaa_pregresid_no_bmi_icc_length_home <- update(eeaa_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)
eeaa_pregresid_no_bmi_icc_armcircm_home <- update(eeaa_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)
eeaa_pregresid_no_bmi_icc_abdom_mean_home <- update(eeaa_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
eeaa_pregresid_no_bmi_icc_headcirc_mean_home <- update(eeaa_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
eeaa_pregresid_no_bmi_icc_total_skin_mean_home <- update(eeaa_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(eeaa_pregresid_no_bmi_icc_weight_home, eeaa_pregresid_no_bmi_icc_length_home, eeaa_pregresid_no_bmi_icc_armcircm_home, eeaa_pregresid_no_bmi_icc_abdom_mean_home, eeaa_pregresid_no_bmi_icc_headcirc_mean_home, eeaa_pregresid_no_bmi_icc_total_skin_mean_home)
No Pre-BMI adjustments, pheno_pregresidage
## No Pre-BMI adjustments, pheno_pregresidage
age_accel_pheno_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ age_accel_pheno_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)
age_accel_pheno_pregresid_no_bmi_icc_length_home <- update(age_accel_pheno_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)
age_accel_pheno_pregresid_no_bmi_icc_armcircm_home <- update(age_accel_pheno_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)
age_accel_pheno_pregresid_no_bmi_icc_abdom_mean_home <- update(age_accel_pheno_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
age_accel_pheno_pregresid_no_bmi_icc_headcirc_mean_home <- update(age_accel_pheno_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
age_accel_pheno_pregresid_no_bmi_icc_total_skin_mean_home <- update(age_accel_pheno_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(age_accel_pheno_pregresid_no_bmi_icc_weight_home, age_accel_pheno_pregresid_no_bmi_icc_length_home, age_accel_pheno_pregresid_no_bmi_icc_armcircm_home, age_accel_pheno_pregresid_no_bmi_icc_abdom_mean_home, age_accel_pheno_pregresid_no_bmi_icc_headcirc_mean_home, age_accel_pheno_pregresid_no_bmi_icc_total_skin_mean_home)
No Pre-BMI adjustments, Grimage
## No Pre-BMI adjustments, grimage
age_accel_grim_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ age_accel_grim_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)
age_accel_grim_pregresid_no_bmi_icc_length_home <- update(age_accel_grim_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)
age_accel_grim_pregresid_no_bmi_icc_armcircm_home <- update(age_accel_grim_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)
age_accel_grim_pregresid_no_bmi_icc_abdom_mean_home <- update(age_accel_grim_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
age_accel_grim_pregresid_no_bmi_icc_headcirc_mean_home <- update(age_accel_grim_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
age_accel_grim_pregresid_no_bmi_icc_total_skin_mean_home <- update(age_accel_grim_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(age_accel_grim_pregresid_no_bmi_icc_weight_home, age_accel_grim_pregresid_no_bmi_icc_length_home, age_accel_grim_pregresid_no_bmi_icc_armcircm_home, age_accel_grim_pregresid_no_bmi_icc_abdom_mean_home, age_accel_grim_pregresid_no_bmi_icc_headcirc_mean_home, age_accel_grim_pregresid_no_bmi_icc_total_skin_mean_home)
## Pre-BMI adjustments, IEAA
ieaa_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ ieaa_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
ieaa_pregresid_bmi_icc_length_home <- update(ieaa_pregresid_bmi_icc_weight_home, icc_length_home ~ .)
ieaa_pregresid_bmi_icc_armcircm_home <- update(ieaa_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)
ieaa_pregresid_bmi_icc_abdom_mean_home <- update(ieaa_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
ieaa_pregresid_bmi_icc_headcirc_mean_home <- update(ieaa_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
ieaa_pregresid_bmi_icc_total_skin_mean_home <- update(ieaa_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(ieaa_pregresid_bmi_icc_weight_home, ieaa_pregresid_bmi_icc_length_home, ieaa_pregresid_bmi_icc_armcircm_home, ieaa_pregresid_bmi_icc_abdom_mean_home, ieaa_pregresid_bmi_icc_headcirc_mean_home, ieaa_pregresid_bmi_icc_total_skin_mean_home, show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S3_IEAA_other.doc"))
| Â | icc weight home | icc length home | icc armcircm home | icc abdom mean home | icc headcirc mean home | icc total skin mean home | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p |
| (Intercept) | -0.10 | -0.25 – 0.06 | 2.68 | 0.008 | -0.14 | -0.30 – 0.02 | 16.79 | <0.001 | -0.02 | -0.18 – 0.14 | 6.49 | <0.001 | -0.01 | -0.16 – 0.15 | 10.71 | <0.001 | -0.21 | -0.36 – -0.06 | 16.67 | <0.001 | 0.10 | -0.07 – 0.26 | 2.34 | 0.020 |
| ieaa_pregresid | 0.04 | -0.06 – 0.15 | 0.82 | 0.415 | 0.07 | -0.04 – 0.18 | 1.24 | 0.217 | 0.04 | -0.07 – 0.15 | 0.77 | 0.442 | 0.00 | -0.10 – 0.11 | 0.02 | 0.980 | 0.06 | -0.04 – 0.16 | 1.17 | 0.241 | 0.03 | -0.08 – 0.14 | 0.56 | 0.578 |
| gestage | 0.18 | 0.07 – 0.29 | 3.31 | 0.001 | 0.09 | -0.02 – 0.20 | 1.68 | 0.094 | 0.20 | 0.09 – 0.32 | 3.61 | <0.001 | 0.15 | 0.04 – 0.26 | 2.78 | 0.006 | 0.15 | 0.04 – 0.25 | 2.81 | 0.005 | 0.08 | -0.04 – 0.19 | 1.35 | 0.180 |
| measurement_age | 0.20 | 0.09 – 0.32 | 3.52 | 0.001 | 0.27 | 0.16 – 0.39 | 4.69 | <0.001 | 0.13 | 0.01 – 0.25 | 2.21 | 0.028 | 0.40 | 0.29 – 0.51 | 7.11 | <0.001 | 0.28 | 0.17 – 0.39 | 5.02 | <0.001 | 0.22 | 0.10 – 0.34 | 3.66 | <0.001 |
| iccsex [Male] | 0.18 | -0.03 – 0.40 | 1.67 | 0.096 | 0.27 | 0.05 – 0.49 | 2.44 | 0.015 | 0.04 | -0.18 – 0.26 | 0.33 | 0.738 | 0.01 | -0.20 – 0.23 | 0.14 | 0.892 | 0.40 | 0.19 – 0.61 | 3.81 | <0.001 | -0.18 | -0.41 – 0.04 | -1.59 | 0.113 |
| ses_pc1 | 0.01 | -0.11 – 0.12 | 0.13 | 0.898 | 0.03 | -0.09 – 0.14 | 0.44 | 0.657 | 0.04 | -0.08 – 0.15 | 0.66 | 0.512 | -0.04 | -0.15 – 0.07 | -0.74 | 0.457 | 0.06 | -0.05 – 0.17 | 1.08 | 0.280 | 0.03 | -0.08 – 0.15 | 0.57 | 0.568 |
| prebmiz | 0.25 | 0.14 – 0.36 | 4.48 | <0.001 | 0.14 | 0.03 – 0.25 | 2.54 | 0.012 | 0.22 | 0.11 – 0.33 | 3.93 | <0.001 | 0.16 | 0.05 – 0.26 | 2.89 | 0.004 | 0.25 | 0.15 – 0.35 | 4.74 | <0.001 | 0.13 | 0.02 – 0.25 | 2.32 | 0.021 |
| Observations | 296 | 296 | 296 | 296 | 296 | 296 | ||||||||||||||||||
| R2 / R2 adjusted | 0.139 / 0.121 | 0.127 / 0.109 | 0.103 / 0.084 | 0.178 / 0.161 | 0.214 / 0.198 | 0.073 / 0.054 | ||||||||||||||||||
## Pre-BMI adjustments, EEAA
eeaa_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ eeaa_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
eeaa_pregresid_bmi_icc_length_home <- update(eeaa_pregresid_bmi_icc_weight_home, icc_length_home ~ .)
eeaa_pregresid_bmi_icc_armcircm_home <- update(eeaa_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)
eeaa_pregresid_bmi_icc_abdom_mean_home <- update(eeaa_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
eeaa_pregresid_bmi_icc_headcirc_mean_home <- update(eeaa_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
eeaa_pregresid_bmi_icc_total_skin_mean_home <- update(eeaa_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(eeaa_pregresid_bmi_icc_weight_home, eeaa_pregresid_bmi_icc_length_home, eeaa_pregresid_bmi_icc_armcircm_home, eeaa_pregresid_bmi_icc_abdom_mean_home, eeaa_pregresid_bmi_icc_headcirc_mean_home, eeaa_pregresid_bmi_icc_total_skin_mean_home, show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S4_EEAA_other.doc"))
| Â | icc weight home | icc length home | icc armcircm home | icc abdom mean home | icc headcirc mean home | icc total skin mean home | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p |
| (Intercept) | -0.09 | -0.25 – 0.06 | 2.65 | 0.009 | -0.14 | -0.30 – 0.02 | 16.73 | <0.001 | -0.02 | -0.18 – 0.14 | 6.47 | <0.001 | -0.01 | -0.16 – 0.15 | 10.73 | <0.001 | -0.21 | -0.36 – -0.06 | 16.62 | <0.001 | 0.10 | -0.06 – 0.26 | 2.32 | 0.021 |
| eeaa_pregresid | -0.07 | -0.18 – 0.04 | -1.31 | 0.192 | -0.05 | -0.16 – 0.06 | -0.88 | 0.378 | 0.04 | -0.07 – 0.15 | 0.68 | 0.498 | -0.06 | -0.16 – 0.05 | -1.07 | 0.285 | -0.04 | -0.15 – 0.06 | -0.79 | 0.429 | -0.04 | -0.15 – 0.07 | -0.73 | 0.468 |
| gestage | 0.19 | 0.08 – 0.29 | 3.34 | 0.001 | 0.10 | -0.01 – 0.21 | 1.72 | 0.086 | 0.21 | 0.09 – 0.32 | 3.63 | <0.001 | 0.15 | 0.04 – 0.26 | 2.79 | 0.006 | 0.15 | 0.05 – 0.26 | 2.85 | 0.005 | 0.08 | -0.03 – 0.19 | 1.36 | 0.173 |
| measurement_age | 0.21 | 0.10 – 0.32 | 3.67 | <0.001 | 0.28 | 0.17 – 0.40 | 4.83 | <0.001 | 0.13 | 0.02 – 0.25 | 2.22 | 0.027 | 0.40 | 0.29 – 0.52 | 7.19 | <0.001 | 0.28 | 0.18 – 0.39 | 5.15 | <0.001 | 0.22 | 0.11 – 0.34 | 3.75 | <0.001 |
| iccsex [Male] | 0.18 | -0.04 – 0.39 | 1.61 | 0.108 | 0.26 | 0.04 – 0.48 | 2.37 | 0.019 | 0.04 | -0.18 – 0.26 | 0.33 | 0.745 | 0.01 | -0.20 – 0.22 | 0.11 | 0.915 | 0.39 | 0.19 – 0.60 | 3.74 | <0.001 | -0.19 | -0.41 – 0.04 | -1.63 | 0.104 |
| ses_pc1 | -0.00 | -0.12 – 0.11 | -0.04 | 0.970 | 0.02 | -0.10 – 0.13 | 0.30 | 0.762 | 0.04 | -0.08 – 0.16 | 0.69 | 0.490 | -0.05 | -0.16 – 0.06 | -0.85 | 0.395 | 0.05 | -0.06 – 0.16 | 0.95 | 0.344 | 0.03 | -0.09 – 0.15 | 0.47 | 0.637 |
| prebmiz | 0.25 | 0.14 – 0.36 | 4.57 | <0.001 | 0.14 | 0.03 – 0.25 | 2.60 | 0.010 | 0.22 | 0.11 – 0.33 | 3.87 | <0.001 | 0.16 | 0.05 – 0.27 | 2.96 | 0.003 | 0.25 | 0.15 – 0.36 | 4.78 | <0.001 | 0.14 | 0.02 – 0.25 | 2.37 | 0.018 |
| Observations | 296 | 296 | 296 | 296 | 296 | 296 | ||||||||||||||||||
| R2 / R2 adjusted | 0.142 / 0.124 | 0.125 / 0.107 | 0.102 / 0.084 | 0.181 / 0.164 | 0.212 / 0.196 | 0.074 / 0.055 | ||||||||||||||||||
## Pre-BMI adjustments, phenoage
age_accel_pheno_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ age_accel_pheno_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
age_accel_pheno_pregresid_bmi_icc_length_home <- update(age_accel_pheno_pregresid_bmi_icc_weight_home, icc_length_home ~ .)
age_accel_pheno_pregresid_bmi_icc_armcircm_home <- update(age_accel_pheno_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)
age_accel_pheno_pregresid_bmi_icc_abdom_mean_home <- update(age_accel_pheno_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
age_accel_pheno_pregresid_bmi_icc_headcirc_mean_home <- update(age_accel_pheno_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
age_accel_pheno_pregresid_bmi_icc_total_skin_mean_home <- update(age_accel_pheno_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(age_accel_pheno_pregresid_bmi_icc_weight_home, age_accel_pheno_pregresid_bmi_icc_length_home, age_accel_pheno_pregresid_bmi_icc_armcircm_home, age_accel_pheno_pregresid_bmi_icc_abdom_mean_home, age_accel_pheno_pregresid_bmi_icc_headcirc_mean_home, age_accel_pheno_pregresid_bmi_icc_total_skin_mean_home, show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S5_pheno_other.doc"))
| Â | icc weight home | icc length home | icc armcircm home | icc abdom mean home | icc headcirc mean home | icc total skin mean home | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p |
| (Intercept) | -0.09 | -0.25 – 0.07 | 2.66 | 0.008 | -0.14 | -0.29 – 0.02 | 16.73 | <0.001 | -0.02 | -0.18 – 0.14 | 6.46 | <0.001 | -0.01 | -0.16 – 0.15 | 10.71 | <0.001 | -0.21 | -0.36 – -0.06 | 16.61 | <0.001 | 0.10 | -0.06 – 0.26 | 2.32 | 0.021 |
| age_accel_pheno_pregresid | -0.03 | -0.14 – 0.08 | -0.55 | 0.582 | -0.03 | -0.14 – 0.08 | -0.53 | 0.600 | 0.02 | -0.09 – 0.13 | 0.33 | 0.739 | -0.00 | -0.11 – 0.10 | -0.09 | 0.926 | -0.02 | -0.12 – 0.09 | -0.33 | 0.745 | -0.01 | -0.12 – 0.10 | -0.20 | 0.841 |
| gestage | 0.18 | 0.08 – 0.29 | 3.33 | 0.001 | 0.10 | -0.01 – 0.21 | 1.71 | 0.088 | 0.21 | 0.09 – 0.32 | 3.64 | <0.001 | 0.15 | 0.04 – 0.26 | 2.78 | 0.006 | 0.15 | 0.05 – 0.26 | 2.84 | 0.005 | 0.08 | -0.04 – 0.19 | 1.36 | 0.175 |
| measurement_age | 0.21 | 0.09 – 0.32 | 3.60 | <0.001 | 0.28 | 0.16 – 0.39 | 4.79 | <0.001 | 0.13 | 0.02 – 0.25 | 2.25 | 0.025 | 0.40 | 0.29 – 0.51 | 7.12 | <0.001 | 0.28 | 0.17 – 0.39 | 5.12 | <0.001 | 0.22 | 0.10 – 0.34 | 3.71 | <0.001 |
| iccsex [Male] | 0.17 | -0.05 – 0.39 | 1.56 | 0.120 | 0.26 | 0.04 – 0.48 | 2.31 | 0.022 | 0.04 | -0.18 – 0.26 | 0.35 | 0.728 | 0.01 | -0.20 – 0.23 | 0.12 | 0.903 | 0.39 | 0.18 – 0.60 | 3.69 | <0.001 | -0.19 | -0.41 – 0.04 | -1.62 | 0.106 |
| ses_pc1 | 0.00 | -0.11 – 0.12 | 0.07 | 0.941 | 0.02 | -0.09 – 0.14 | 0.37 | 0.709 | 0.04 | -0.08 – 0.15 | 0.64 | 0.524 | -0.04 | -0.15 – 0.07 | -0.75 | 0.454 | 0.06 | -0.05 – 0.16 | 1.02 | 0.309 | 0.03 | -0.09 – 0.15 | 0.54 | 0.589 |
| prebmiz | 0.25 | 0.14 – 0.36 | 4.49 | <0.001 | 0.14 | 0.03 – 0.25 | 2.55 | 0.011 | 0.22 | 0.11 – 0.33 | 3.92 | <0.001 | 0.16 | 0.05 – 0.26 | 2.89 | 0.004 | 0.25 | 0.15 – 0.35 | 4.74 | <0.001 | 0.13 | 0.02 – 0.25 | 2.33 | 0.021 |
| Observations | 296 | 296 | 296 | 296 | 296 | 296 | ||||||||||||||||||
| R2 / R2 adjusted | 0.138 / 0.120 | 0.124 / 0.105 | 0.101 / 0.082 | 0.178 / 0.161 | 0.211 / 0.194 | 0.073 / 0.053 | ||||||||||||||||||
## Pre-BMI adjustments, grimage
age_accel_grim_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ age_accel_grim_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
age_accel_grim_pregresid_bmi_icc_length_home <- update(age_accel_grim_pregresid_bmi_icc_weight_home, icc_length_home ~ .)
age_accel_grim_pregresid_bmi_icc_armcircm_home <- update(age_accel_grim_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)
age_accel_grim_pregresid_bmi_icc_abdom_mean_home <- update(age_accel_grim_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
age_accel_grim_pregresid_bmi_icc_headcirc_mean_home <- update(age_accel_grim_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
age_accel_grim_pregresid_bmi_icc_total_skin_mean_home <- update(age_accel_grim_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(age_accel_grim_pregresid_bmi_icc_weight_home, age_accel_grim_pregresid_bmi_icc_length_home, age_accel_grim_pregresid_bmi_icc_armcircm_home, age_accel_grim_pregresid_bmi_icc_abdom_mean_home, age_accel_grim_pregresid_bmi_icc_headcirc_mean_home, age_accel_grim_pregresid_bmi_icc_total_skin_mean_home, show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S6_grim_other.doc"))
| Â | icc weight home | icc length home | icc armcircm home | icc abdom mean home | icc headcirc mean home | icc total skin mean home | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p |
| (Intercept) | -0.10 | -0.26 – 0.05 | 2.63 | 0.009 | -0.15 | -0.31 – 0.01 | 16.78 | <0.001 | -0.02 | -0.18 – 0.14 | 6.46 | <0.001 | -0.01 | -0.16 – 0.14 | 10.71 | <0.001 | -0.22 | -0.37 – -0.07 | 16.66 | <0.001 | 0.09 | -0.07 – 0.26 | 2.31 | 0.021 |
| age_accel_grim_pregresid | 0.08 | -0.03 – 0.19 | 1.42 | 0.155 | 0.10 | -0.01 – 0.21 | 1.84 | 0.068 | 0.05 | -0.06 – 0.16 | 0.85 | 0.397 | 0.00 | -0.10 – 0.11 | 0.08 | 0.935 | 0.09 | -0.01 – 0.20 | 1.74 | 0.083 | 0.03 | -0.08 – 0.14 | 0.53 | 0.597 |
| gestage | 0.19 | 0.08 – 0.30 | 3.37 | 0.001 | 0.10 | -0.01 – 0.21 | 1.77 | 0.078 | 0.21 | 0.10 – 0.32 | 3.65 | <0.001 | 0.15 | 0.04 – 0.26 | 2.78 | 0.006 | 0.15 | 0.05 – 0.26 | 2.89 | 0.004 | 0.08 | -0.03 – 0.19 | 1.37 | 0.170 |
| measurement_age | 0.20 | 0.08 – 0.31 | 3.39 | 0.001 | 0.26 | 0.15 – 0.38 | 4.54 | <0.001 | 0.13 | 0.01 – 0.24 | 2.15 | 0.033 | 0.40 | 0.29 – 0.51 | 7.06 | <0.001 | 0.27 | 0.16 – 0.38 | 4.87 | <0.001 | 0.22 | 0.10 – 0.34 | 3.61 | <0.001 |
| iccsex [Male] | 0.20 | -0.02 – 0.41 | 1.77 | 0.078 | 0.28 | 0.07 – 0.50 | 2.56 | 0.011 | 0.04 | -0.18 – 0.27 | 0.38 | 0.702 | 0.02 | -0.20 – 0.23 | 0.14 | 0.887 | 0.41 | 0.21 – 0.62 | 3.92 | <0.001 | -0.18 | -0.40 – 0.05 | -1.56 | 0.121 |
| ses_pc1 | 0.01 | -0.11 – 0.12 | 0.11 | 0.910 | 0.02 | -0.09 – 0.14 | 0.42 | 0.676 | 0.04 | -0.08 – 0.15 | 0.64 | 0.525 | -0.04 | -0.15 – 0.07 | -0.75 | 0.457 | 0.06 | -0.05 – 0.17 | 1.06 | 0.291 | 0.03 | -0.08 – 0.15 | 0.56 | 0.579 |
| prebmiz | 0.24 | 0.13 – 0.35 | 4.28 | <0.001 | 0.13 | 0.02 – 0.24 | 2.30 | 0.022 | 0.22 | 0.10 – 0.33 | 3.79 | <0.001 | 0.16 | 0.05 – 0.26 | 2.85 | 0.005 | 0.24 | 0.13 – 0.34 | 4.50 | <0.001 | 0.13 | 0.02 – 0.24 | 2.24 | 0.026 |
| Observations | 296 | 296 | 296 | 296 | 296 | 296 | ||||||||||||||||||
| R2 / R2 adjusted | 0.143 / 0.125 | 0.133 / 0.115 | 0.103 / 0.084 | 0.178 / 0.161 | 0.218 / 0.202 | 0.073 / 0.054 | ||||||||||||||||||
Overall conclusion: GrimAge looks the best, now going to try to dissect each of the individual "clocks" that make up GrimAge.
No Pre-BMI adjustments, dn_am_adm_adj_age
## No Pre-BMI adjustments, dn_am_adm_adj_age
dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_adm_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)
dn_am_adm_adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_adm_adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_adm_adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_adm_adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_adm_adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home, dn_am_adm_adj_age_pregresid_no_bmi_icc_length_home, dn_am_adm_adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_adm_adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_adm_adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_adm_adj_age_pregresid_no_bmi_icc_total_skin_mean_home)
## Pre-BMI adjustments, dn_am_adm_adj_age
dn_am_adm_adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_adm_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
dn_am_adm_adj_age_pregresid_bmi_icc_length_home <- update(dn_am_adm_adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_adm_adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_adm_adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_adm_adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_adm_adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_adm_adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_adm_adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_adm_adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_adm_adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_adm_adj_age_pregresid_bmi_icc_weight_home, dn_am_adm_adj_age_pregresid_bmi_icc_length_home, dn_am_adm_adj_age_pregresid_bmi_icc_armcircm_home, dn_am_adm_adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_adm_adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_adm_adj_age_pregresid_bmi_icc_total_skin_mean_home, show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S7_adm_other.doc"))
| Â | icc weight home | icc length home | icc armcircm home | icc abdom mean home | icc headcirc mean home | icc total skin mean home | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p |
| (Intercept) | -0.10 | -0.26 – 0.06 | 2.52 | 0.012 | -0.15 | -0.30 – 0.01 | 16.65 | <0.001 | -0.02 | -0.18 – 0.14 | 6.36 | <0.001 | -0.01 | -0.16 – 0.15 | 10.68 | <0.001 | -0.21 | -0.36 – -0.06 | 16.50 | <0.001 | 0.09 | -0.07 – 0.26 | 2.26 | 0.025 |
| dn_am_adm_adj_age_pregresid | 0.09 | -0.02 – 0.20 | 1.64 | 0.102 | 0.14 | 0.03 – 0.25 | 2.50 | 0.013 | 0.08 | -0.03 – 0.19 | 1.40 | 0.162 | -0.00 | -0.11 – 0.10 | -0.05 | 0.959 | 0.09 | -0.01 – 0.19 | 1.72 | 0.086 | 0.04 | -0.07 – 0.16 | 0.75 | 0.452 |
| gestage | 0.19 | 0.08 – 0.30 | 3.47 | 0.001 | 0.11 | -0.00 – 0.22 | 1.93 | 0.055 | 0.21 | 0.10 – 0.32 | 3.74 | <0.001 | 0.15 | 0.04 – 0.26 | 2.77 | 0.006 | 0.16 | 0.05 – 0.26 | 2.98 | 0.003 | 0.08 | -0.03 – 0.20 | 1.42 | 0.157 |
| measurement_age | 0.20 | 0.09 – 0.32 | 3.56 | <0.001 | 0.27 | 0.16 – 0.39 | 4.77 | <0.001 | 0.13 | 0.02 – 0.25 | 2.24 | 0.026 | 0.40 | 0.29 – 0.51 | 7.13 | <0.001 | 0.28 | 0.17 – 0.39 | 5.09 | <0.001 | 0.22 | 0.10 – 0.34 | 3.69 | <0.001 |
| iccsex [Male] | 0.19 | -0.03 – 0.41 | 1.73 | 0.085 | 0.28 | 0.06 – 0.50 | 2.54 | 0.012 | 0.04 | -0.18 – 0.26 | 0.38 | 0.706 | 0.01 | -0.20 – 0.23 | 0.13 | 0.895 | 0.41 | 0.20 – 0.61 | 3.86 | <0.001 | -0.18 | -0.40 – 0.05 | -1.57 | 0.117 |
| ses_pc1 | -0.00 | -0.11 – 0.11 | -0.00 | 0.998 | 0.01 | -0.10 – 0.13 | 0.25 | 0.805 | 0.03 | -0.08 – 0.15 | 0.54 | 0.588 | -0.04 | -0.15 – 0.07 | -0.74 | 0.459 | 0.05 | -0.06 – 0.16 | 0.93 | 0.352 | 0.03 | -0.09 – 0.15 | 0.50 | 0.615 |
| prebmiz | 0.24 | 0.13 – 0.35 | 4.34 | <0.001 | 0.13 | 0.02 – 0.24 | 2.34 | 0.020 | 0.21 | 0.10 – 0.33 | 3.81 | <0.001 | 0.16 | 0.05 – 0.26 | 2.88 | 0.004 | 0.24 | 0.14 – 0.35 | 4.59 | <0.001 | 0.13 | 0.02 – 0.24 | 2.25 | 0.025 |
| Observations | 296 | 296 | 296 | 296 | 296 | 296 | ||||||||||||||||||
| R2 / R2 adjusted | 0.145 / 0.127 | 0.141 / 0.124 | 0.107 / 0.088 | 0.178 / 0.161 | 0.218 / 0.202 | 0.074 / 0.055 | ||||||||||||||||||
No Pre-BMI adjustments, dn_am_b2m_adj_age
## No Pre-BMI adjustments, dn_am_b2m_adj_age
dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_b2m_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)
dn_am_b2m_adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_b2m_adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_b2m_adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_b2m_adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_b2m_adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home, dn_am_b2m_adj_age_pregresid_no_bmi_icc_length_home, dn_am_b2m_adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_b2m_adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_b2m_adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_b2m_adj_age_pregresid_no_bmi_icc_total_skin_mean_home)
## Pre-BMI adjustments, dn_am_b2m_adj_age
dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_b2m_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
dn_am_b2m_adj_age_pregresid_bmi_icc_length_home <- update(dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_b2m_adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_b2m_adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_b2m_adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_b2m_adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home, dn_am_b2m_adj_age_pregresid_bmi_icc_length_home, dn_am_b2m_adj_age_pregresid_bmi_icc_armcircm_home, dn_am_b2m_adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_b2m_adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_b2m_adj_age_pregresid_bmi_icc_total_skin_mean_home, show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S8_b2m_other.doc"))
| Â | icc weight home | icc length home | icc armcircm home | icc abdom mean home | icc headcirc mean home | icc total skin mean home | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p |
| (Intercept) | -0.09 | -0.25 – 0.06 | 2.65 | 0.008 | -0.14 | -0.30 – 0.02 | 16.71 | <0.001 | -0.02 | -0.18 – 0.14 | 6.45 | <0.001 | -0.01 | -0.16 – 0.14 | 10.72 | <0.001 | -0.21 | -0.36 – -0.06 | 16.64 | <0.001 | 0.10 | -0.07 – 0.26 | 2.31 | 0.022 |
| dn_am_b2m_adj_age_pregresid | -0.02 | -0.12 – 0.09 | -0.31 | 0.757 | 0.02 | -0.09 – 0.13 | 0.38 | 0.706 | 0.04 | -0.07 – 0.15 | 0.79 | 0.433 | -0.02 | -0.13 – 0.08 | -0.42 | 0.675 | -0.05 | -0.15 – 0.06 | -0.87 | 0.383 | 0.04 | -0.07 – 0.15 | 0.69 | 0.492 |
| gestage | 0.18 | 0.08 – 0.29 | 3.33 | 0.001 | 0.10 | -0.01 – 0.21 | 1.73 | 0.085 | 0.21 | 0.10 – 0.32 | 3.65 | <0.001 | 0.15 | 0.04 – 0.26 | 2.77 | 0.006 | 0.15 | 0.05 – 0.25 | 2.82 | 0.005 | 0.08 | -0.03 – 0.19 | 1.38 | 0.168 |
| measurement_age | 0.21 | 0.09 – 0.32 | 3.59 | <0.001 | 0.28 | 0.16 – 0.39 | 4.77 | <0.001 | 0.13 | 0.02 – 0.25 | 2.26 | 0.025 | 0.40 | 0.29 – 0.51 | 7.13 | <0.001 | 0.28 | 0.17 – 0.39 | 5.12 | <0.001 | 0.22 | 0.10 – 0.34 | 3.70 | <0.001 |
| iccsex [Male] | 0.18 | -0.04 – 0.40 | 1.64 | 0.102 | 0.27 | 0.05 – 0.48 | 2.39 | 0.018 | 0.03 | -0.19 – 0.26 | 0.31 | 0.760 | 0.01 | -0.20 – 0.23 | 0.14 | 0.892 | 0.40 | 0.19 – 0.60 | 3.77 | <0.001 | -0.18 | -0.41 – 0.04 | -1.61 | 0.108 |
| ses_pc1 | 0.00 | -0.11 – 0.12 | 0.08 | 0.940 | 0.02 | -0.09 – 0.14 | 0.42 | 0.676 | 0.04 | -0.08 – 0.16 | 0.68 | 0.500 | -0.04 | -0.15 – 0.07 | -0.77 | 0.441 | 0.05 | -0.05 – 0.16 | 0.97 | 0.330 | 0.04 | -0.08 – 0.15 | 0.59 | 0.554 |
| prebmiz | 0.25 | 0.14 – 0.36 | 4.49 | <0.001 | 0.14 | 0.03 – 0.25 | 2.52 | 0.012 | 0.22 | 0.11 – 0.33 | 3.90 | <0.001 | 0.16 | 0.05 – 0.26 | 2.90 | 0.004 | 0.25 | 0.15 – 0.36 | 4.76 | <0.001 | 0.13 | 0.02 – 0.24 | 2.30 | 0.022 |
| Observations | 296 | 296 | 296 | 296 | 296 | 296 | ||||||||||||||||||
| R2 / R2 adjusted | 0.137 / 0.120 | 0.123 / 0.105 | 0.103 / 0.084 | 0.178 / 0.161 | 0.212 / 0.196 | 0.074 / 0.055 | ||||||||||||||||||
No Pre-BMI adjustments, dn_am_cystatin_c_adj_age
## No Pre-BMI adjustments, dn_am_cystatin_c_adj_age
dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_cystatin_c_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)
dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home, dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_length_home, dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_total_skin_mean_home)
## Pre-BMI adjustments, dn_am_cystatin_c_adj_age
dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_cystatin_c_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
dn_am_cystatin_c_adj_age_pregresid_bmi_icc_length_home <- update(dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_cystatin_c_adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_cystatin_c_adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_cystatin_c_adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_cystatin_c_adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home, dn_am_cystatin_c_adj_age_pregresid_bmi_icc_length_home, dn_am_cystatin_c_adj_age_pregresid_bmi_icc_armcircm_home, dn_am_cystatin_c_adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_cystatin_c_adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_cystatin_c_adj_age_pregresid_bmi_icc_total_skin_mean_home, show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S9_cystatin_other.doc"))
| Â | icc weight home | icc length home | icc armcircm home | icc abdom mean home | icc headcirc mean home | icc total skin mean home | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p |
| (Intercept) | -0.10 | -0.25 – 0.06 | 2.62 | 0.009 | -0.14 | -0.30 – 0.01 | 16.70 | <0.001 | -0.02 | -0.18 – 0.14 | 6.45 | <0.001 | -0.01 | -0.16 – 0.14 | 10.69 | <0.001 | -0.21 | -0.36 – -0.06 | 16.58 | <0.001 | 0.09 | -0.07 – 0.25 | 2.28 | 0.024 |
| dn_am_cystatin_c_adj_age_pregresid | 0.03 | -0.07 – 0.14 | 0.63 | 0.532 | 0.07 | -0.04 – 0.18 | 1.29 | 0.197 | 0.01 | -0.10 – 0.12 | 0.19 | 0.852 | 0.03 | -0.07 – 0.14 | 0.61 | 0.542 | 0.06 | -0.05 – 0.16 | 1.09 | 0.275 | 0.07 | -0.04 – 0.18 | 1.20 | 0.230 |
| gestage | 0.19 | 0.08 – 0.30 | 3.36 | 0.001 | 0.10 | -0.01 – 0.21 | 1.78 | 0.077 | 0.21 | 0.09 – 0.32 | 3.63 | <0.001 | 0.15 | 0.05 – 0.26 | 2.81 | 0.005 | 0.15 | 0.05 – 0.26 | 2.89 | 0.004 | 0.08 | -0.03 – 0.19 | 1.42 | 0.158 |
| measurement_age | 0.20 | 0.09 – 0.32 | 3.53 | <0.001 | 0.27 | 0.16 – 0.39 | 4.69 | <0.001 | 0.13 | 0.02 – 0.25 | 2.25 | 0.025 | 0.40 | 0.29 – 0.51 | 7.07 | <0.001 | 0.28 | 0.17 – 0.39 | 5.03 | <0.001 | 0.22 | 0.10 – 0.33 | 3.63 | <0.001 |
| iccsex [Male] | 0.18 | -0.03 – 0.40 | 1.67 | 0.096 | 0.27 | 0.05 – 0.49 | 2.45 | 0.015 | 0.04 | -0.19 – 0.26 | 0.32 | 0.752 | 0.02 | -0.19 – 0.23 | 0.16 | 0.871 | 0.40 | 0.19 – 0.61 | 3.82 | <0.001 | -0.18 | -0.40 – 0.05 | -1.56 | 0.120 |
| ses_pc1 | 0.01 | -0.11 – 0.12 | 0.14 | 0.887 | 0.03 | -0.09 – 0.14 | 0.49 | 0.624 | 0.04 | -0.08 – 0.15 | 0.64 | 0.524 | -0.04 | -0.15 – 0.07 | -0.70 | 0.485 | 0.06 | -0.05 – 0.17 | 1.11 | 0.267 | 0.04 | -0.08 – 0.16 | 0.64 | 0.524 |
| prebmiz | 0.25 | 0.14 – 0.36 | 4.49 | <0.001 | 0.14 | 0.03 – 0.25 | 2.56 | 0.011 | 0.22 | 0.11 – 0.33 | 3.93 | <0.001 | 0.16 | 0.05 – 0.26 | 2.90 | 0.004 | 0.25 | 0.15 – 0.35 | 4.76 | <0.001 | 0.13 | 0.02 – 0.25 | 2.35 | 0.020 |
| Observations | 296 | 296 | 296 | 296 | 296 | 296 | ||||||||||||||||||
| R2 / R2 adjusted | 0.138 / 0.120 | 0.128 / 0.110 | 0.101 / 0.082 | 0.179 / 0.162 | 0.214 / 0.197 | 0.077 / 0.058 | ||||||||||||||||||
No Pre-BMI adjustments, dn_am_gdf15adj_age_pregresid
## No Pre-BMI adjustments, dn_am_gdf15adj_age
dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_gdf15adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)
dn_am_gdf15adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_gdf15adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_gdf15adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_gdf15adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_gdf15adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home, dn_am_gdf15adj_age_pregresid_no_bmi_icc_length_home, dn_am_gdf15adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_gdf15adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_gdf15adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_gdf15adj_age_pregresid_no_bmi_icc_total_skin_mean_home)
## Pre-BMI adjustments, dn_am_gdf15adj_age
dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_gdf15adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
dn_am_gdf15adj_age_pregresid_bmi_icc_length_home <- update(dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_gdf15adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_gdf15adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_gdf15adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_gdf15adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home, dn_am_gdf15adj_age_pregresid_bmi_icc_length_home, dn_am_gdf15adj_age_pregresid_bmi_icc_armcircm_home, dn_am_gdf15adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_gdf15adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_gdf15adj_age_pregresid_bmi_icc_total_skin_mean_home, show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S10_gdf15_other.doc"))
| Â | icc weight home | icc length home | icc armcircm home | icc abdom mean home | icc headcirc mean home | icc total skin mean home | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p |
| (Intercept) | -0.09 | -0.25 – 0.06 | 2.65 | 0.009 | -0.14 | -0.30 – 0.02 | 16.74 | <0.001 | -0.02 | -0.18 – 0.14 | 6.46 | <0.001 | -0.01 | -0.16 – 0.14 | 10.71 | <0.001 | -0.21 | -0.36 – -0.06 | 16.66 | <0.001 | 0.10 | -0.07 – 0.26 | 2.32 | 0.021 |
| dn_am_gdf15adj_age_pregresid | 0.01 | -0.10 – 0.12 | 0.23 | 0.819 | 0.05 | -0.06 – 0.15 | 0.83 | 0.409 | -0.02 | -0.13 – 0.09 | -0.36 | 0.722 | -0.05 | -0.15 – 0.06 | -0.91 | 0.363 | 0.06 | -0.04 – 0.17 | 1.20 | 0.229 | 0.01 | -0.10 – 0.13 | 0.24 | 0.809 |
| gestage | 0.19 | 0.08 – 0.29 | 3.33 | 0.001 | 0.10 | -0.01 – 0.21 | 1.71 | 0.088 | 0.21 | 0.09 – 0.32 | 3.63 | <0.001 | 0.15 | 0.04 – 0.26 | 2.80 | 0.006 | 0.15 | 0.05 – 0.25 | 2.83 | 0.005 | 0.08 | -0.04 – 0.19 | 1.36 | 0.175 |
| measurement_age | 0.21 | 0.09 – 0.32 | 3.57 | <0.001 | 0.28 | 0.16 – 0.39 | 4.75 | <0.001 | 0.13 | 0.02 – 0.25 | 2.28 | 0.023 | 0.40 | 0.29 – 0.51 | 7.17 | <0.001 | 0.28 | 0.17 – 0.39 | 5.07 | <0.001 | 0.22 | 0.10 – 0.34 | 3.70 | <0.001 |
| iccsex [Male] | 0.18 | -0.04 – 0.40 | 1.64 | 0.102 | 0.27 | 0.05 – 0.48 | 2.39 | 0.017 | 0.03 | -0.19 – 0.26 | 0.31 | 0.759 | 0.01 | -0.20 – 0.23 | 0.13 | 0.893 | 0.40 | 0.19 – 0.60 | 3.77 | <0.001 | -0.18 | -0.41 – 0.04 | -1.61 | 0.109 |
| ses_pc1 | 0.01 | -0.11 – 0.12 | 0.11 | 0.909 | 0.03 | -0.09 – 0.14 | 0.46 | 0.646 | 0.03 | -0.08 – 0.15 | 0.59 | 0.553 | -0.05 | -0.16 – 0.06 | -0.82 | 0.414 | 0.06 | -0.05 – 0.17 | 1.13 | 0.260 | 0.03 | -0.08 – 0.15 | 0.57 | 0.571 |
| prebmiz | 0.25 | 0.14 – 0.36 | 4.48 | <0.001 | 0.14 | 0.03 – 0.25 | 2.58 | 0.010 | 0.22 | 0.11 – 0.33 | 3.90 | <0.001 | 0.15 | 0.05 – 0.26 | 2.84 | 0.005 | 0.25 | 0.15 – 0.36 | 4.80 | <0.001 | 0.13 | 0.02 – 0.25 | 2.33 | 0.020 |
| Observations | 296 | 296 | 296 | 296 | 296 | 296 | ||||||||||||||||||
| R2 / R2 adjusted | 0.137 / 0.119 | 0.125 / 0.107 | 0.101 / 0.083 | 0.180 / 0.163 | 0.214 / 0.198 | 0.073 / 0.053 | ||||||||||||||||||
No Pre-BMI adjustments, dn_am_leptin_adj_age_pregresid
## No Pre-BMI adjustments, dn_am_leptin_adj_age
dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_leptin_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)
dn_am_leptin_adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_leptin_adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_leptin_adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_leptin_adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_leptin_adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home, dn_am_leptin_adj_age_pregresid_no_bmi_icc_length_home, dn_am_leptin_adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_leptin_adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_leptin_adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_leptin_adj_age_pregresid_no_bmi_icc_total_skin_mean_home)
## Pre-BMI adjustments, dn_am_leptin_adj_age
dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_leptin_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
dn_am_leptin_adj_age_pregresid_bmi_icc_length_home <- update(dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_leptin_adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_leptin_adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_leptin_adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_leptin_adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home, dn_am_leptin_adj_age_pregresid_bmi_icc_length_home, dn_am_leptin_adj_age_pregresid_bmi_icc_armcircm_home, dn_am_leptin_adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_leptin_adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_leptin_adj_age_pregresid_bmi_icc_total_skin_mean_home, show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S11_leptin_other.doc"))
| Â | icc weight home | icc length home | icc armcircm home | icc abdom mean home | icc headcirc mean home | icc total skin mean home | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p |
| (Intercept) | -0.10 | -0.25 – 0.06 | 2.50 | 0.013 | -0.14 | -0.30 – 0.02 | 16.49 | <0.001 | -0.02 | -0.18 – 0.14 | 6.46 | <0.001 | -0.01 | -0.16 – 0.14 | 10.41 | <0.001 | -0.21 | -0.36 – -0.06 | 16.25 | <0.001 | 0.09 | -0.07 – 0.26 | 2.15 | 0.032 |
| dn_am_leptin_adj_age_pregresid | 0.04 | -0.07 – 0.15 | 0.74 | 0.461 | 0.01 | -0.10 – 0.12 | 0.19 | 0.851 | -0.03 | -0.14 – 0.08 | -0.49 | 0.625 | 0.08 | -0.03 – 0.18 | 1.40 | 0.162 | 0.07 | -0.03 – 0.18 | 1.39 | 0.166 | 0.05 | -0.06 – 0.17 | 0.96 | 0.339 |
| gestage | 0.19 | 0.08 – 0.30 | 3.41 | 0.001 | 0.10 | -0.01 – 0.21 | 1.73 | 0.085 | 0.20 | 0.09 – 0.31 | 3.51 | 0.001 | 0.16 | 0.05 – 0.27 | 2.97 | 0.003 | 0.16 | 0.06 – 0.27 | 3.03 | 0.003 | 0.09 | -0.03 – 0.20 | 1.50 | 0.136 |
| measurement_age | 0.21 | 0.09 – 0.32 | 3.59 | <0.001 | 0.28 | 0.16 – 0.39 | 4.78 | <0.001 | 0.13 | 0.02 – 0.25 | 2.26 | 0.024 | 0.40 | 0.29 – 0.51 | 7.16 | <0.001 | 0.28 | 0.17 – 0.39 | 5.13 | <0.001 | 0.22 | 0.10 – 0.34 | 3.72 | <0.001 |
| iccsex [Male] | 0.18 | -0.03 – 0.40 | 1.66 | 0.098 | 0.27 | 0.05 – 0.49 | 2.39 | 0.017 | 0.03 | -0.19 – 0.25 | 0.29 | 0.768 | 0.02 | -0.19 – 0.23 | 0.17 | 0.863 | 0.40 | 0.19 – 0.61 | 3.81 | <0.001 | -0.18 | -0.41 – 0.04 | -1.59 | 0.114 |
| ses_pc1 | 0.01 | -0.11 – 0.12 | 0.13 | 0.896 | 0.02 | -0.09 – 0.14 | 0.40 | 0.688 | 0.04 | -0.08 – 0.15 | 0.60 | 0.548 | -0.04 | -0.15 – 0.07 | -0.68 | 0.497 | 0.06 | -0.05 – 0.17 | 1.10 | 0.271 | 0.04 | -0.08 – 0.15 | 0.60 | 0.552 |
| prebmiz | 0.25 | 0.14 – 0.35 | 4.44 | <0.001 | 0.14 | 0.03 – 0.25 | 2.52 | 0.012 | 0.22 | 0.11 – 0.33 | 3.94 | <0.001 | 0.15 | 0.05 – 0.26 | 2.83 | 0.005 | 0.25 | 0.14 – 0.35 | 4.68 | <0.001 | 0.13 | 0.02 – 0.24 | 2.28 | 0.023 |
| Observations | 296 | 296 | 296 | 296 | 296 | 296 | ||||||||||||||||||
| R2 / R2 adjusted | 0.139 / 0.121 | 0.123 / 0.105 | 0.102 / 0.083 | 0.183 / 0.166 | 0.216 / 0.199 | 0.075 / 0.056 | ||||||||||||||||||
No Pre-BMI adjustments, dn_am_packyrs_adj_age
## No Pre-BMI adjustments, dn_am_packyrs_adj_age
dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_packyrs_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)
dn_am_packyrs_adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_packyrs_adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_packyrs_adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_packyrs_adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_packyrs_adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home, dn_am_packyrs_adj_age_pregresid_no_bmi_icc_length_home, dn_am_packyrs_adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_packyrs_adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_packyrs_adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_packyrs_adj_age_pregresid_no_bmi_icc_total_skin_mean_home)
## Pre-BMI adjustments, dn_am_packyrs_adj_age
dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_packyrs_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
dn_am_packyrs_adj_age_pregresid_bmi_icc_length_home <- update(dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_packyrs_adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_packyrs_adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_packyrs_adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_packyrs_adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home, dn_am_packyrs_adj_age_pregresid_bmi_icc_length_home, dn_am_packyrs_adj_age_pregresid_bmi_icc_armcircm_home, dn_am_packyrs_adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_packyrs_adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_packyrs_adj_age_pregresid_bmi_icc_total_skin_mean_home, show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S12_packyears_other.doc"))
| Â | icc weight home | icc length home | icc armcircm home | icc abdom mean home | icc headcirc mean home | icc total skin mean home | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p |
| (Intercept) | -0.10 | -0.26 – 0.06 | 2.66 | 0.008 | -0.14 | -0.30 – 0.02 | 16.71 | <0.001 | -0.02 | -0.18 – 0.14 | 6.47 | <0.001 | -0.01 | -0.16 – 0.14 | 10.72 | <0.001 | -0.21 | -0.36 – -0.06 | 16.65 | <0.001 | 0.10 | -0.06 – 0.26 | 2.32 | 0.021 |
| dn_am_packyrs_adj_age_pregresid | 0.06 | -0.05 – 0.17 | 1.09 | 0.278 | -0.00 | -0.11 – 0.11 | -0.07 | 0.948 | 0.01 | -0.10 – 0.13 | 0.26 | 0.798 | 0.02 | -0.08 – 0.13 | 0.41 | 0.679 | 0.06 | -0.04 – 0.17 | 1.16 | 0.248 | -0.01 | -0.13 – 0.10 | -0.22 | 0.823 |
| gestage | 0.19 | 0.08 – 0.29 | 3.34 | 0.001 | 0.10 | -0.01 – 0.21 | 1.72 | 0.087 | 0.21 | 0.09 – 0.32 | 3.63 | <0.001 | 0.15 | 0.04 – 0.26 | 2.78 | 0.006 | 0.15 | 0.05 – 0.26 | 2.85 | 0.005 | 0.08 | -0.03 – 0.19 | 1.36 | 0.174 |
| measurement_age | 0.20 | 0.08 – 0.31 | 3.41 | 0.001 | 0.28 | 0.16 – 0.39 | 4.74 | <0.001 | 0.13 | 0.01 – 0.25 | 2.21 | 0.028 | 0.40 | 0.29 – 0.51 | 7.00 | <0.001 | 0.27 | 0.16 – 0.38 | 4.91 | <0.001 | 0.22 | 0.10 – 0.34 | 3.70 | <0.001 |
| iccsex [Male] | 0.19 | -0.03 – 0.41 | 1.72 | 0.086 | 0.27 | 0.05 – 0.48 | 2.38 | 0.018 | 0.04 | -0.19 – 0.26 | 0.33 | 0.745 | 0.02 | -0.19 – 0.23 | 0.17 | 0.869 | 0.41 | 0.20 – 0.61 | 3.85 | <0.001 | -0.19 | -0.41 – 0.04 | -1.62 | 0.106 |
| ses_pc1 | 0.01 | -0.10 – 0.12 | 0.16 | 0.875 | 0.02 | -0.09 – 0.14 | 0.39 | 0.697 | 0.04 | -0.08 – 0.15 | 0.64 | 0.523 | -0.04 | -0.15 – 0.07 | -0.72 | 0.471 | 0.06 | -0.05 – 0.17 | 1.10 | 0.273 | 0.03 | -0.09 – 0.15 | 0.54 | 0.593 |
| prebmiz | 0.24 | 0.13 – 0.35 | 4.38 | <0.001 | 0.14 | 0.03 – 0.25 | 2.53 | 0.012 | 0.22 | 0.11 – 0.33 | 3.89 | <0.001 | 0.15 | 0.05 – 0.26 | 2.84 | 0.005 | 0.25 | 0.14 – 0.35 | 4.63 | <0.001 | 0.13 | 0.02 – 0.25 | 2.34 | 0.020 |
| Observations | 296 | 296 | 296 | 296 | 296 | 296 | ||||||||||||||||||
| R2 / R2 adjusted | 0.141 / 0.123 | 0.123 / 0.105 | 0.101 / 0.082 | 0.178 / 0.161 | 0.214 / 0.198 | 0.073 / 0.053 | ||||||||||||||||||
No Pre-BMI adjustments, dn_am_pai1adj_age
## No Pre-BMI adjustments, dn_am_pai1adj_age
dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_pai1adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)
dn_am_pai1adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_pai1adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_pai1adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_pai1adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_pai1adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home, dn_am_pai1adj_age_pregresid_no_bmi_icc_length_home, dn_am_pai1adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_pai1adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_pai1adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_pai1adj_age_pregresid_no_bmi_icc_total_skin_mean_home)
## Pre-BMI adjustments, dn_am_pai1adj_age
dn_am_pai1adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_pai1adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
dn_am_pai1adj_age_pregresid_bmi_icc_length_home <- update(dn_am_pai1adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_pai1adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_pai1adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_pai1adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_pai1adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_pai1adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_pai1adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_pai1adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_pai1adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_pai1adj_age_pregresid_bmi_icc_weight_home, dn_am_pai1adj_age_pregresid_bmi_icc_length_home, dn_am_pai1adj_age_pregresid_bmi_icc_armcircm_home, dn_am_pai1adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_pai1adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_pai1adj_age_pregresid_bmi_icc_total_skin_mean_home, show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S13_pai1_other.doc"))
| Â | icc weight home | icc length home | icc armcircm home | icc abdom mean home | icc headcirc mean home | icc total skin mean home | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p |
| (Intercept) | -0.09 | -0.25 – 0.06 | 2.65 | 0.009 | -0.14 | -0.30 – 0.02 | 16.75 | <0.001 | -0.02 | -0.18 – 0.14 | 6.44 | <0.001 | -0.01 | -0.16 – 0.15 | 10.67 | <0.001 | -0.21 | -0.36 – -0.06 | 16.59 | <0.001 | 0.10 | -0.07 – 0.26 | 2.31 | 0.021 |
| dn_am_pai1adj_age_pregresid | 0.01 | -0.11 – 0.12 | 0.09 | 0.929 | 0.04 | -0.07 – 0.16 | 0.73 | 0.464 | -0.02 | -0.13 – 0.10 | -0.29 | 0.774 | -0.06 | -0.17 – 0.06 | -0.97 | 0.332 | 0.01 | -0.10 – 0.12 | 0.21 | 0.833 | -0.01 | -0.13 – 0.11 | -0.11 | 0.914 |
| gestage | 0.19 | 0.08 – 0.29 | 3.33 | 0.001 | 0.09 | -0.02 – 0.21 | 1.69 | 0.092 | 0.21 | 0.09 – 0.32 | 3.64 | <0.001 | 0.15 | 0.05 – 0.26 | 2.82 | 0.005 | 0.15 | 0.05 – 0.26 | 2.83 | 0.005 | 0.08 | -0.03 – 0.19 | 1.37 | 0.173 |
| measurement_age | 0.21 | 0.09 – 0.32 | 3.55 | <0.001 | 0.27 | 0.16 – 0.39 | 4.67 | <0.001 | 0.14 | 0.02 – 0.25 | 2.29 | 0.023 | 0.41 | 0.30 – 0.52 | 7.20 | <0.001 | 0.28 | 0.17 – 0.39 | 5.05 | <0.001 | 0.22 | 0.10 – 0.34 | 3.70 | <0.001 |
| iccsex [Male] | 0.18 | -0.04 – 0.40 | 1.64 | 0.101 | 0.27 | 0.05 – 0.49 | 2.42 | 0.016 | 0.03 | -0.19 – 0.26 | 0.30 | 0.768 | 0.01 | -0.20 – 0.22 | 0.09 | 0.925 | 0.40 | 0.19 – 0.61 | 3.77 | <0.001 | -0.18 | -0.41 – 0.04 | -1.61 | 0.108 |
| ses_pc1 | 0.00 | -0.11 – 0.12 | 0.07 | 0.941 | 0.01 | -0.10 – 0.13 | 0.22 | 0.823 | 0.04 | -0.08 – 0.16 | 0.67 | 0.501 | -0.03 | -0.14 – 0.08 | -0.52 | 0.607 | 0.05 | -0.06 – 0.17 | 0.96 | 0.338 | 0.03 | -0.09 – 0.15 | 0.56 | 0.577 |
| prebmiz | 0.25 | 0.13 – 0.36 | 4.31 | <0.001 | 0.13 | 0.02 – 0.24 | 2.27 | 0.024 | 0.23 | 0.11 – 0.34 | 3.87 | <0.001 | 0.17 | 0.06 – 0.28 | 3.04 | 0.003 | 0.25 | 0.14 – 0.35 | 4.53 | <0.001 | 0.13 | 0.02 – 0.25 | 2.28 | 0.024 |
| Observations | 296 | 296 | 296 | 296 | 296 | 296 | ||||||||||||||||||
| R2 / R2 adjusted | 0.137 / 0.119 | 0.124 / 0.106 | 0.101 / 0.082 | 0.181 / 0.163 | 0.210 / 0.194 | 0.072 / 0.053 | ||||||||||||||||||
No Pre-BMI adjustments, dn_am_timp1adj_age
## No Pre-BMI adjustments, dn_am_timp1adj_age
dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_timp1adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)
dn_am_timp1adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_timp1adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_timp1adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_timp1adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_timp1adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home, dn_am_timp1adj_age_pregresid_no_bmi_icc_length_home, dn_am_timp1adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_timp1adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_timp1adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_timp1adj_age_pregresid_no_bmi_icc_total_skin_mean_home)
## Pre-BMI adjustments, dn_am_timp1adj_age
dn_am_timp1adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_timp1adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
dn_am_timp1adj_age_pregresid_bmi_icc_length_home <- update(dn_am_timp1adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_timp1adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_timp1adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_timp1adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_timp1adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_timp1adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_timp1adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_timp1adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_timp1adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_timp1adj_age_pregresid_bmi_icc_weight_home, dn_am_timp1adj_age_pregresid_bmi_icc_length_home, dn_am_timp1adj_age_pregresid_bmi_icc_armcircm_home, dn_am_timp1adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_timp1adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_timp1adj_age_pregresid_bmi_icc_total_skin_mean_home, show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S14_timp1_other.doc"))
| Â | icc weight home | icc length home | icc armcircm home | icc abdom mean home | icc headcirc mean home | icc total skin mean home | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p |
| (Intercept) | -0.10 | -0.25 – 0.06 | 2.65 | 0.008 | -0.15 | -0.30 – 0.01 | 16.79 | <0.001 | -0.02 | -0.18 – 0.14 | 6.47 | <0.001 | -0.01 | -0.16 – 0.14 | 10.73 | <0.001 | -0.21 | -0.36 – -0.06 | 16.64 | <0.001 | 0.10 | -0.07 – 0.26 | 2.32 | 0.021 |
| dn_am_timp1adj_age_pregresid | 0.04 | -0.07 – 0.15 | 0.71 | 0.479 | 0.09 | -0.02 – 0.20 | 1.57 | 0.118 | 0.04 | -0.07 – 0.15 | 0.63 | 0.526 | 0.05 | -0.05 – 0.16 | 0.96 | 0.337 | 0.06 | -0.05 – 0.16 | 1.06 | 0.289 | 0.01 | -0.10 – 0.13 | 0.24 | 0.814 |
| gestage | 0.19 | 0.08 – 0.29 | 3.33 | 0.001 | 0.10 | -0.01 – 0.21 | 1.72 | 0.087 | 0.21 | 0.09 – 0.32 | 3.63 | <0.001 | 0.15 | 0.04 – 0.26 | 2.78 | 0.006 | 0.15 | 0.05 – 0.26 | 2.84 | 0.005 | 0.08 | -0.03 – 0.19 | 1.36 | 0.174 |
| measurement_age | 0.21 | 0.09 – 0.32 | 3.57 | <0.001 | 0.28 | 0.16 – 0.39 | 4.77 | <0.001 | 0.13 | 0.02 – 0.25 | 2.26 | 0.025 | 0.40 | 0.29 – 0.51 | 7.12 | <0.001 | 0.28 | 0.17 – 0.39 | 5.10 | <0.001 | 0.22 | 0.10 – 0.34 | 3.70 | <0.001 |
| iccsex [Male] | 0.19 | -0.03 – 0.40 | 1.69 | 0.091 | 0.28 | 0.06 – 0.50 | 2.52 | 0.012 | 0.04 | -0.18 – 0.26 | 0.36 | 0.721 | 0.02 | -0.19 – 0.23 | 0.21 | 0.833 | 0.41 | 0.20 – 0.61 | 3.84 | <0.001 | -0.18 | -0.41 – 0.04 | -1.59 | 0.114 |
| ses_pc1 | 0.01 | -0.11 – 0.12 | 0.14 | 0.887 | 0.03 | -0.09 – 0.14 | 0.50 | 0.619 | 0.04 | -0.08 – 0.15 | 0.67 | 0.506 | -0.04 | -0.15 – 0.07 | -0.68 | 0.495 | 0.06 | -0.05 – 0.17 | 1.10 | 0.272 | 0.03 | -0.08 – 0.15 | 0.56 | 0.574 |
| prebmiz | 0.24 | 0.14 – 0.35 | 4.42 | <0.001 | 0.14 | 0.03 – 0.24 | 2.44 | 0.015 | 0.22 | 0.11 – 0.33 | 3.88 | <0.001 | 0.15 | 0.05 – 0.26 | 2.82 | 0.005 | 0.25 | 0.14 – 0.35 | 4.66 | <0.001 | 0.13 | 0.02 – 0.25 | 2.30 | 0.022 |
| Observations | 296 | 296 | 296 | 296 | 296 | 296 | ||||||||||||||||||
| R2 / R2 adjusted | 0.139 / 0.121 | 0.130 / 0.112 | 0.102 / 0.083 | 0.180 / 0.163 | 0.213 / 0.197 | 0.073 / 0.053 | ||||||||||||||||||
No Pre-BMI adjustments, dn_am_tl_adj_age
## No Pre-BMI adjustments, dn_am_tl_adj_age
dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_tl_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)
dn_am_tl_adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_tl_adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_tl_adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_tl_adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_tl_adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home, dn_am_tl_adj_age_pregresid_no_bmi_icc_length_home, dn_am_tl_adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_tl_adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_tl_adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_tl_adj_age_pregresid_no_bmi_icc_total_skin_mean_home)
## Pre-BMI adjustments, dn_am_tl_adj_age
dn_am_tl_adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_tl_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
dn_am_tl_adj_age_pregresid_bmi_icc_length_home <- update(dn_am_tl_adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)
dn_am_tl_adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_tl_adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)
dn_am_tl_adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_tl_adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)
dn_am_tl_adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_tl_adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)
dn_am_tl_adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_tl_adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)
sjPlot::tab_model(dn_am_tl_adj_age_pregresid_bmi_icc_weight_home, dn_am_tl_adj_age_pregresid_bmi_icc_length_home, dn_am_tl_adj_age_pregresid_bmi_icc_armcircm_home, dn_am_tl_adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_tl_adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_tl_adj_age_pregresid_bmi_icc_total_skin_mean_home, show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S15_tl_other.doc"))
| Â | icc weight home | icc length home | icc armcircm home | icc abdom mean home | icc headcirc mean home | icc total skin mean home | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p | std. Beta | standardized CI | Statistic | p |
| (Intercept) | -0.09 | -0.25 – 0.07 | 2.61 | 0.010 | -0.13 | -0.29 – 0.03 | 16.76 | <0.001 | -0.02 | -0.18 – 0.14 | 6.47 | <0.001 | -0.00 | -0.16 – 0.15 | 10.68 | <0.001 | -0.20 | -0.35 – -0.05 | 16.58 | <0.001 | 0.10 | -0.07 – 0.26 | 2.32 | 0.021 |
| dn_am_tl_adj_age_pregresid | 0.05 | -0.06 – 0.16 | 0.96 | 0.339 | 0.13 | 0.02 – 0.23 | 2.30 | 0.022 | -0.02 | -0.13 – 0.09 | -0.38 | 0.702 | 0.05 | -0.05 – 0.16 | 0.97 | 0.331 | 0.06 | -0.04 – 0.16 | 1.16 | 0.247 | 0.01 | -0.11 – 0.12 | 0.09 | 0.930 |
| gestage | 0.19 | 0.08 – 0.30 | 3.38 | 0.001 | 0.10 | -0.01 – 0.21 | 1.84 | 0.067 | 0.21 | 0.09 – 0.32 | 3.61 | <0.001 | 0.15 | 0.05 – 0.26 | 2.83 | 0.005 | 0.15 | 0.05 – 0.26 | 2.90 | 0.004 | 0.08 | -0.03 – 0.19 | 1.37 | 0.173 |
| measurement_age | 0.21 | 0.09 – 0.32 | 3.57 | <0.001 | 0.27 | 0.16 – 0.39 | 4.77 | <0.001 | 0.13 | 0.02 – 0.25 | 2.28 | 0.024 | 0.40 | 0.29 – 0.51 | 7.12 | <0.001 | 0.28 | 0.17 – 0.39 | 5.09 | <0.001 | 0.22 | 0.10 – 0.34 | 3.71 | <0.001 |
| iccsex [Male] | 0.17 | -0.04 – 0.39 | 1.56 | 0.119 | 0.25 | 0.03 – 0.46 | 2.22 | 0.027 | 0.04 | -0.18 – 0.26 | 0.34 | 0.736 | 0.01 | -0.21 – 0.22 | 0.06 | 0.954 | 0.39 | 0.18 – 0.60 | 3.67 | <0.001 | -0.18 | -0.41 – 0.04 | -1.61 | 0.108 |
| ses_pc1 | 0.00 | -0.11 – 0.12 | 0.06 | 0.950 | 0.02 | -0.09 – 0.13 | 0.32 | 0.749 | 0.04 | -0.08 – 0.15 | 0.64 | 0.524 | -0.04 | -0.15 – 0.07 | -0.78 | 0.436 | 0.05 | -0.05 – 0.16 | 0.99 | 0.321 | 0.03 | -0.08 – 0.15 | 0.55 | 0.586 |
| prebmiz | 0.24 | 0.14 – 0.35 | 4.42 | <0.001 | 0.13 | 0.02 – 0.24 | 2.41 | 0.017 | 0.22 | 0.11 – 0.33 | 3.94 | <0.001 | 0.15 | 0.05 – 0.26 | 2.82 | 0.005 | 0.25 | 0.14 – 0.35 | 4.66 | <0.001 | 0.13 | 0.02 – 0.25 | 2.31 | 0.021 |
| Observations | 296 | 296 | 296 | 296 | 296 | 296 | ||||||||||||||||||
| R2 / R2 adjusted | 0.140 / 0.122 | 0.139 / 0.121 | 0.101 / 0.083 | 0.181 / 0.164 | 0.214 / 0.198 | 0.072 / 0.053 | ||||||||||||||||||
# Get residuals
#
length_basic <- lm(icc_length_home ~ gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)
headcirc_basic <-update(length_basic, icc_headcirc_mean_home ~ . )
weight_basic <-update(length_basic, icc_weight_home ~ . )
length_resids <-broom::augment(length_basic)$.resid
headcirc_resids <-broom::augment(headcirc_basic)$.resid
weight_resids <-broom::augment(weight_basic)$.resid
all_but_leptin_gest_age_no_meas <- lm(gestage ~ iccsex + ses_pc1 + prebmiz ,data = combined_data)
ga_resids <-broom::augment(all_but_leptin_gest_age_no_meas)$.resid
leptin_plot <-ggplot(data = combined_data, mapping = aes(x = dn_am_leptin_adj_age_pregresid, y = ga_resids)) +
geom_point(color = 'firebrick', alpha = 0.7) +
geom_smooth(method = "lm", color = "black") +
labs(x = "DNAmLeptin EEA", y = "Postnatal Measured Weight (residuals)")+
theme_pubr()+
annotate("text", x = -2800, y = -50, label=expression(beta * "= -0.15, p-value = 0.009")); leptin_plot
adm_weight_plot <-ggplot(data = combined_data, mapping = aes(x = dn_am_adm_adj_age_pregresid, y = weight_resids)) +
geom_point(color = 'steelblue', alpha = 0.7) +
geom_smooth(method = "lm", color = "black") +
labs(x = "DNAmADM EEA", y = "Postnatal Measured Weight (residuals)")+
theme_pubr()+
annotate("text", x = -28, y = -1.3, label=expression(beta * "= 0.09, p-value = 0.102")); adm_weight_plot
adm_length_plot <-ggplot(data = combined_data, mapping = aes(x = dn_am_adm_adj_age_pregresid, y = length_resids)) +
geom_point(color = 'steelblue', alpha = 0.7) +
geom_smooth(method = "lm", color = "black") +
labs(x = "DNAmADM EEA", y = "Postnatal Length (residuals)")+
theme_pubr()+
annotate("text", x = -28, y = -10, label=expression(beta * "= 0.14, p-value = 0.013")); adm_length_plot
adm_headcirc_plot <-ggplot(data = combined_data, mapping = aes(x = dn_am_adm_adj_age_pregresid, y = headcirc_resids)) +
geom_point(color = 'steelblue', alpha = 0.7) +
geom_smooth(method = "lm", color = "black") +
labs(x = "DNAmADM EEA", y = "Postnatal Head Circumference (residuals)")+
theme_pubr()+
annotate("text", x = -28, y = -4.5, label=expression(beta * "= 0.09, p-value = 0.086")); adm_headcirc_plot
ggarrange(leptin_plot, adm_length_plot, adm_headcirc_plot, adm_weight_plot,
labels = c("A", "B", "C", "D"),
ncol = 2, nrow = 2)
# Get residuals
length_resids <-broom::augment(length_basic)$.resid
headcirc_resids <-broom::augment(headcirc_basic)$.resid
TL_headcirc_plot <-broom::augment(headcirc_basic)$.resid
grim_length_plot <-ggplot(data = combined_data, mapping = aes(x = age_accel_grim_pregresid, y = length_resids)) +
geom_point(color = 'purple', alpha = 0.7) +
geom_smooth(method = "lm", color = "black") +
labs(x = "DNAmGrim EEA", y = "Postnatal Length (residuals)")+
theme_pubr()+
annotate("text", x = -3.5, y = -10, label=expression(beta * "= 0.14, p-value = 0.013")); grim_length_plot
grim_headcirc_plot <-ggplot(data = combined_data, mapping = aes(x = age_accel_grim_pregresid, y = headcirc_resids)) +
geom_point(color = 'purple', alpha = 0.7) +
geom_smooth(method = "lm", color = "black") +
labs(x = "DNAmGrim EEA", y = "Postnatal Head Circumference (residuals)")+
theme_pubr()+
annotate("text", x = -3.5, y = -4.5, label=expression(beta * "= 0.09, p-value = 0.086")); grim_headcirc_plot
TL_headcirc_plot <-ggplot(data = combined_data, mapping = aes(x = dn_am_tl_adj_age_pregresid, y = headcirc_resids)) +
geom_point(color = 'darkgreen', alpha = 0.7) +
geom_smooth(method = "lm", color = "black") +
labs(x = "DNAmTL EEA", y = "Postnatal Head Circumference (residuals)")+
theme_pubr()+
annotate("text", x = -0.125, y = -4.5, label=expression(beta * "= 0.09, p-value = 0.086")); TL_headcirc_plot
ggarrange(grim_length_plot, grim_headcirc_plot, TL_headcirc_plot,
labels = c("A", "B", "C"),
ncol = 3, nrow = 1)